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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some notes on the Colorado datasets (by Toma\u017e \u0160olc)\n",
      "\n",
      "# Data\n",
      "\n",
      "We are working on the \"cu/rssi\" dataset. Original source is CROWDAD site here:\n",
      "\n",
      "http://crawdad.org/cu/rssi/20090528/text/\n",
      "\n",
      "In the eWINE repository, the data is located at `eWINE/linkQualityEstimation/datasets/trace2_Colorado/data/`.\n",
      "\n",
      "## Interpretation\n",
      "\n",
      "Data is presented as a set of text files. Each file consists of lines like this:\n",
      "\n",
      "    24.3892672440343 56.0953146612789 dev1 down 16 0 -79 -93 -62 -69 -87\n",
      "    \n",
      "**Note that the description of the data on CRAWDAD doesn't match the actual content of the files.** So already from this point on, we are guessing as to what the numbers in files actually mean.\n",
      "\n",
      "Based on the `eWINE/linkQualityEstimation/datasets/trace2_Colorado/pythonScript/perLink.py`, our interpretation of the data is as follows:\n",
      "\n",
      "Each line describes one packet, transmitted by a node and received by 5 \"monitors\". Columns in the files:\n",
      "\n",
      " * 0-1: Some kind of coordinate of the transmitting node.\n",
      " * 2: Unknown. Maybe wireless network device for nodes that have more than one.\n",
      " * 3: Orientation of the antenna.\n",
      " * 4: Transmit power in dBm.\n",
      " * 5: Packet sequence number.\n",
      " * 6-10: RSSI in dBm for each of the 5 monitors."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class CURow():\n",
      "    def __init__(self, line):\n",
      "        fi = line.split()\n",
      "    \n",
      "        self.x = float(fi[0])    \n",
      "        self.y = float(fi[1])\n",
      "\n",
      "        self.dev = fi[2]\n",
      "        self.ant = fi[3]\n",
      "\n",
      "        self.pw = int(fi[4])\n",
      "\n",
      "        self.seq = int(fi[5])\n",
      "\n",
      "        self.rssi = map(float, fi[6:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `perLink.py` script treats each line in the file as information about 5 packets, sent over 5 \"links\". Each link is identified using the transmitting node, transmission power and received monitor."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import glob\n",
      "from collections import defaultdict\n",
      "\n",
      "packets_in_link = defaultdict(list)\n",
      "\n",
      "for dataset in glob.glob(\"/home/avian/dev/eWINE/linkQualityEstimation/datasets/trace2_Colorado/data/*.txt\"):\n",
      "    for line in open(dataset):\n",
      "        row = CURow(line)\n",
      "        \n",
      "        for i, rssi in enumerate(row.rssi):\n",
      "            link_id = (dataset, row.x, row.y, row.dev, row.ant, row.pw, i)\n",
      "            \n",
      "            packets_in_link[link_id].append(row)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For each link, a set number of packets is assumed to be sent. `perLink.py` gets this number from the file from which the data is read."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expected_n_packets = {}\n",
      "for link_id in packets_in_link.keys():\n",
      "    \n",
      "    dataset = link_id[0]\n",
      "    if 'omni_16dbm' in dataset:\n",
      "        n = 500\n",
      "    elif 'omni_variable_txpower' in dataset:\n",
      "        n = 200\n",
      "    elif 'dir_variable_txpower' in dataset:\n",
      "        n = 200\n",
      "    else:\n",
      "        assert False\n",
      "        \n",
      "    expected_n_packets[link_id] = n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The dataset has some rows missing, which is interpreted as lost packets. PRR for each link is calculated by dividing the expected number of packets on the link and the number of packets that is present in the file.\n",
      "\n",
      "**Note: dataset description on the CRAWDAD doesn't mention anything about lost packets. It is very unusual that lost packets would be represented as a whole missing line. What about cases where just one of the 5 monitors did not receive the packet?**\n",
      "\n",
      "Average RSSI for each link is calculated by calculating the mean over the monitor's RSSI."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_links = len(packets_in_link)\n",
      "\n",
      "mean_rssi = empty(n_links)\n",
      "prr = empty(n_links)\n",
      "\n",
      "for i, (link_id, rows) in enumerate(packets_in_link.items()):\n",
      "    \n",
      "    n_received = len(rows)\n",
      "    n_sent = expected_n_packets[link_id]\n",
      "    \n",
      "    prr[i] = float(n_received)/n_sent\n",
      "    \n",
      "    monitor = link_id[6]\n",
      "    mean_rssi[i] = sum(row.rssi[monitor] for row in rows)/float(n_received)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting average RSSI versus PRR for all links:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(mean_rssi, prr, '.')\n",
      "xlabel(\"Mean RSSI [dBm]\")\n",
      "ylabel(\"PRR\")\n",
      "grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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Ss7W1aWNmW5tbJxmjm5EAK+VP3kY/RErg6UdIJXmlvB49lCA1NWlmVVvrzUbb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\nQRAEQcjy/6hsZcXnJ1VCAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0b0bdc43d0>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}